Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is how you make AI answer from your knowledge instead of guessing from its training. It retrieves the relevant pieces of your documents and data at question time and grounds the model’s answer in them — with citations — so responses are accurate and checkable.
We build production RAG systems over your content — policies, manuals, tickets, product data — with the retrieval quality, access control, and evaluation that separate a reliable assistant from a plausible liar.
Problems we solve
AI that does not know your business
A general model has never seen your policies, products, or history, so it cannot answer questions about them without guessing. RAG gives it your knowledge.
Answers you cannot trust or verify
Without grounding and citations, you cannot tell if an answer is right. RAG returns sources so answers are checkable.
Poor retrieval sinks the whole thing
If the system retrieves the wrong context, the answer is wrong no matter how good the model. Retrieval quality is the hard part.
How we approach it
Quality retrieval over your content
We ingest and chunk your documents and data, build search that returns genuinely relevant context, and tune it — because retrieval quality, not the model, usually decides the outcome.
Grounded, cited answers
The model answers only from retrieved context and returns sources, so users can verify — and the system can say "I don’t know" instead of inventing.
Access-controlled and evaluated
Retrieval respects who is allowed to see what, and we set up evaluation so quality is measured and maintained, not assumed.
What you get
- Ingestion and chunking of your documents/data
- Tuned retrieval (search) returning relevant context
- Grounded generation with citations and "I don’t know"
- Access control on retrieved content
- An evaluation harness for answer quality
- Integration into your app or workflow
Technologies & integrations
Our delivery process
- 01Ingest
Chunk and index your documents and data.
- 02Retrieve
Build and tune relevant-context retrieval.
- 03Ground
Generate answers only from retrieved sources.
- 04Secure
Enforce access control on content.
- 05Evaluate
Measure quality and iterate.
Apparel Globe — AI/RAG-assisted operations
Frequently asked questions
Why RAG instead of fine-tuning a model?
RAG keeps your knowledge current and access-controlled, cites sources, and avoids baking data into a model. It is usually the right first choice; fine-tuning has a place for style or narrow tasks, not for knowledge you update.
How do you keep it from answering when it should not?
The system answers only from retrieved context and is designed to say "I don’t know" when nothing relevant is found, rather than inventing.
Can it respect who can see which documents?
Yes — retrieval enforces access control so users only get answers grounded in content they are allowed to see.